{"title":"Incentive hierarchies intensify competition for attention: A study of online reviews","authors":"Baojun Zhang , Zili Zhang , Kee-Hung Lai , Ziqiong Zhang","doi":"10.1016/j.dss.2024.114293","DOIUrl":null,"url":null,"abstract":"<div><p>While many online platforms use incentive hierarchies to stimulate consumers to generate more online reviews, the extent to which these hierarchies influence reviewer behavior is not fully understood. This study, drawing on image motivation theory and consumer attention theory, takes a novel approach to investigate whether reviewers strategically adjust their review behavior after reaching higher ranks in a hierarchy. We use data from rank change timestamps on platforms to accurately identify reviewers' ranks when posting reviews and then employ a quasi-natural experimental design for causal inference. Additionally, we use Fisher's permutation test to explore the different effects at various ranks. The empirical results reveal that reviewers tend to increase their review length and insert more pictures into their reviews after they reach higher ranks. Reviewers at lower ranks tend to submit more extreme ratings upon rank advancement, whereas their higher-ranking counterparts do not demonstrate significant change. Unlike ratings, reviewers tend to consistently increase the sentiment intensity of their expressions in text after reaching higher ranks. Furthermore, our findings indicate that the magnitude of changes in reviewing behavior only shows an increasing trend in the early stages of rank progression. These insights contribute to a better understanding of the efficacy of incentive hierarchies and offer practical implications for decision-making by platform managers.</p></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"185 ","pages":"Article 114293"},"PeriodicalIF":6.7000,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Decision Support Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S016792362400126X","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
While many online platforms use incentive hierarchies to stimulate consumers to generate more online reviews, the extent to which these hierarchies influence reviewer behavior is not fully understood. This study, drawing on image motivation theory and consumer attention theory, takes a novel approach to investigate whether reviewers strategically adjust their review behavior after reaching higher ranks in a hierarchy. We use data from rank change timestamps on platforms to accurately identify reviewers' ranks when posting reviews and then employ a quasi-natural experimental design for causal inference. Additionally, we use Fisher's permutation test to explore the different effects at various ranks. The empirical results reveal that reviewers tend to increase their review length and insert more pictures into their reviews after they reach higher ranks. Reviewers at lower ranks tend to submit more extreme ratings upon rank advancement, whereas their higher-ranking counterparts do not demonstrate significant change. Unlike ratings, reviewers tend to consistently increase the sentiment intensity of their expressions in text after reaching higher ranks. Furthermore, our findings indicate that the magnitude of changes in reviewing behavior only shows an increasing trend in the early stages of rank progression. These insights contribute to a better understanding of the efficacy of incentive hierarchies and offer practical implications for decision-making by platform managers.
期刊介绍:
The common thread of articles published in Decision Support Systems is their relevance to theoretical and technical issues in the support of enhanced decision making. The areas addressed may include foundations, functionality, interfaces, implementation, impacts, and evaluation of decision support systems (DSSs).